What the data for this category looks like
Data sources include contract ledgers, cost accounting documents, annual audit reports, and customer settlement vouchers for engineering consulting projects. Updates are synchronized per individual project cycle, annual, or semi-annual financial report deadlines. Document structures include structured tables (with fields such as project number, service type, billing base, and more), PDF-format audit report paragraphs, and Excel-format cost detail files. Fields include project code, service category, tax-included project cost, labor proportion, management fee rate, and settlement cycle. Common units are RMB yuan, percentage, and calendar days.
Constraints for Vector Models and Indexing
Engineering consulting financial reports contain both structured detailed tables and unstructured audit report paragraphs. This requires vector models and indexing to support retrieval of mixed-type data. Project cycles vary widely, and update rhythms are uneven. Some historical data is updated infrequently. New project data requires real-time synchronization. This requires indexes to support flexible switching between incremental and full updates. Individual audit reports have long text lengths. This requires vector embeddings to support long text chunking. Field details differ slightly across projects. This requires indexes to support custom field mapping and matching rules. These characteristics jointly constrain vector model dimension selection, index sharding strategies, and configuration directions for recall rules. Targeted adjustments are needed to fit business requirements.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Alibaba text-embedding-v3 or open-source vector models with dimension ≥1024 | Engineering consulting financial reports include structured details and long-text audit paragraphs. High-dimensional models can cover finer-grained semantic and field features |
chunk_size | 800–1200 characters | Individual audit report paragraphs are mostly 500-2000 characters long. This chunking range preserves complete semantic units while avoiding token limit exceedance |
index_incremental_update | Enabled | Engineering consulting projects are settled per cycle. New settlement data requires real-time synchronization to indexes. Incremental updates avoid resource consumption from full index rebuilding |
retrieval_top_k | Top 8-12 results | Financial report analysis requires coverage of cross-project benchmarking and single-project details. An appropriate number of recall results balances retrieval accuracy and response speed |
structured_field_index | Enabled | Engineering consulting financial reports include key structured fields such as project number and settlement amount. Enabling this supports accurate field matching retrieval |
embedding_batch_size | 32-64 items per batch | Financial report data volume per batch is large. This batch processing range balances embedding efficiency and memory usage |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. Testing on sample datasets is recommended before finalizing settings.
Three Common Errors
- Symptom: When selecting a vector model other than ada-002, the
undefined model must match "^(texterror appears. Cause: The whitelist or path mapping for the corresponding vector model was not added in the system configuration. This causes the system to fail to recognize non-official default vector model identifiers. - Symptom: Semantic retrieval scores are high, but the generated financial report analysis results have large deviations. The issue persists after replacing the vector model. Cause: The
structured_field_indexconfiguration was not enabled. Only text semantic recall is relied on, which cannot accurately match key structured fields such as project numbers and billing bases in engineering consulting financial reports. - Symptom: When a single project data entry corresponds to multiple vector chunks, retrieval results contain duplicate chunks and cannot be grouped by original project. This issue is particularly prominent in version v4.8.7. Cause: A reasonable value for the
chunk_overlapparameter was not configured, or the recall strategy for grouping by original document was not enabled. This causes chunked vector chunks to not be bound to their original data ownership.
How to Verify Correct Configuration
- Upload a single engineering consulting financial report sample, check the running logs of the vector embedding task, and confirm that the calling parameters of the selected vector model match the configuration items.
- Initiate a structured field retrieval, such as querying financial report data for a specified project number, and confirm that the retrieval results include matching structured field content.
- Upload new project settlement data, check the index update logs, and confirm that the incremental index synchronization was successful.
- Test a long-text audit report with multiple chunks, and confirm that the recall results' chunk ownership matches the original document.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.